BMW Must Re-Prioritize EV Investments as Asia Emerges as the Primary Driver of Global EV Growth

Introduction

This interactive report is designed for BMW’s Chief Marketing Officer (CMO).
Using BMW’s worldwide sales records from 2010–2024, the goal is to:

1. Identify where global EV demand is accelerating or stagnating
   
2. Understand which models and fuel types drive growth in each region
   
3. Analyze regional EV readiness
   
4. Provide actionable guidance for BMW’s 2026 EV marketing and investment strategy

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Question 1 – Where Should BMW Expand EV Investments Across Regions?

Part A — Regional EV Sales Trends (2010–2024)

The first chart compares annual EV sales across six global regions.
Each line shows the total sales volume for all BMW EV models in that region and year.

How to explore this chart

• Hover over any point to see the region, year, and total sales
  
• Click a region in the legend to highlight it
  
• Double-click a region in the legend to isolate it
  
• Drag on the plot area to zoom into a specific time window; use the home icon to reset

Why this matters for the CMO

This view shows where EV demand is gaining momentum and where it is stagnating.
Regions with strong upward trends (for example, Asia or the Middle East) are candidates for expansion in 2026, while flat or declining regions suggest a strategy that stabilizes or optimizes existing investment.

---

p1 <- sd_region_year %>%
plot_ly(
x = ~Year,
y = ~total_sales,
color = ~Region,
colors = region_colors,
type = "scatter",
mode = "lines+markers",
hovertemplate = paste(
"<b>%{customdata[1]}</b><br>",
"Year: %{x}<br>",
"Total sales: %{y}<extra></extra>"
),
customdata = ~Region
) %>%
layout(
title = list(
text = "Regional EV Sales Trends (2010–2024)",
x = 0
),
xaxis = list(title = "Year"),
yaxis = list(title = "Total EV Sales")
)


controls <- filter_select(
id = "region_filter",
label = "Focus on one region (controls linked charts below):",
sharedData = sd_region_year,
group = ~Region
)

bscols(widths = c(4, 8), controls, p1)

Focus on one region (controls linked charts below):

Question 2 – Which Models Drive Growth in High-Potential Regions?

Part A — Top Models in Selected Region (Linked to Question 1)

After focusing on a region using the selector above, the chart updates automatically to show that region’s top 10 models by total EV sales.

How to explore this chart

• Use the “Focus on one region” control above Question 1
  
• Hover over any bar to see the exact model name and total sales
  
• Compare the shape of the model portfolio across different regions by switching the region filter

Why this matters for the CMO

Once high-growth regions are identified, the CMO needs to understand which specific models are driving that growth.
These are the models that may deserve:

• More marketing budget
  
• Additional dealer support
  
• Region-specific campaigns or launch events

Models with low sales in high-growth regions may signal positioning issues or gaps in awareness.

fuel_palette_hex <- c(
  "Electric" = "#A8C5FF",  # pastel blue
  "Hybrid"   = "#C8B0E8",  # pastel purple
  "Petrol"   = "#B7E4C7",  # pastel green
  "Diesel"   = "#FFE8A3"   # pastel yellow
)

p2 <- sd_model_region %>%
  plot_ly(
    x = ~total_sales,
    y = ~reorder(Model, total_sales),
    type = "bar",
    orientation = "h",
    color = ~Fuel_Type,
    colors = fuel_palette_hex,
    hovertemplate = "<b>%{y}</b><br>Fuel Type: %{color}<br>Total Sales: %{x:,}<extra></extra>"
  ) %>%
  
  layout(
    title = list(text = "Top 10 Models in Selected Region", x = 0),
    xaxis = list(title = "Total Sales", tickformat = ","),
    yaxis = list(title = "", tickfont = list(size = 14)),
    plot_bgcolor = "#FFFFFF",
    paper_bgcolor = "#FFFFFF",
    margin = list(l = 120, r = 40)
  )

p2

Question 3 – How Ready Is Each Region for EV Adoption? (Fuel-Type Mix)

Part A — Distribution of Fuel Types by Region

This chart compares the fuel-type mix in each region: Electric, Hybrid, Petrol, and Diesel.
A region with a higher share of Electric and Hybrid vehicles is generally more ready for EV-focused campaigns and infrastructure partnerships.

How to explore this chart

• Hover over each stacked bar to see the fuel type, count, and total sales
  
• Continue using the region selector above Question 1 to focus on a single region across all linked charts
  
• Compare regions to see where EV and Hybrid represent a substantial share

Why this matters for the CMO

Fuel-type composition is a strong indicator of regional EV readiness.
This chart therefore helps the CMO align:

• Market education
  
• EV launches
  
• Infrastructure collaborations

with regional readiness.

p3 <- sd_fuel_region %>%
plot_ly(
x = ~Region,
y = ~n,
color = ~Fuel_Type,
type  = "bar",
hovertemplate = paste(
"<b>%{x}</b><br>",
"Fuel type: %{legendgroup}<br>",
"Vehicle count: %{y}<extra></extra>"
)
) %>%
layout(
barmode = "stack",
title   = list(text = "Fuel-Type Mix by Region", x = 0),
xaxis   = list(title = ""),
yaxis   = list(title = "Number of Vehicles")
)

p3

Question 4 – How Does EV Sales Intensity Vary Across Regions Over Time?

Part A — Regional EV Sales Heatmap (2010–2024)

This heatmap visualizes BMW’s EV sales intensity across regions and years.
Darker colors represent higher total sales in a given region-year pair.

How to explore this chart

• Hover over any cell to see the exact region, year, and total EV sales
  
• Focus on darker bands to identify high-momentum years
  
• Compare patterns across regions to detect acceleration or slowdown

Why this matters for the CMO

This view makes it easy to identify:

• Regions that are consistently strong EV performers
  
• Regions with emerging EV momentum
  
• Key inflection points in EV adoption
  
• Where BMW should intensify marketing efforts in the 2026 cycle

Understanding temporal sales concentration helps the CMO allocate budgets to regions with sustained or accelerating adoption.

# 自定义颜色渐变
custom_palette <- list(
  c(0, "#FFFFFF"),
  c(0.25, "#E9F2FA"),
  c(0.5, "#C4DDF2"),
  c(0.75, "#8BBCE3"),
  c(1, "#508ECF")
)

# 重建 region-year 数据（确保干净无 NA）
region_year <- bmw %>%
  group_by(Region, Year) %>%
  summarise(total_sales = sum(Sales_Volume), .groups = "drop")

# 创建交互式热力图
p4 <- plot_ly(
  data = region_year,
  x = ~Year,
  y = ~Region,
  z = ~total_sales,
  type = "heatmap",
  colorscale = custom_palette,
  hovertemplate = paste(
    "<b>Region:</b> %{y}<br>",
    "Year: %{x}<br>",
    "Sales: %{z}<extra></extra>"
  )
) %>%
  layout(
    title = "BMW EV Sales Heatmap (Interactive)",
    xaxis = list(title = "Year"),
    yaxis = list(title = "Region")
  )

p4

Price–Sales Correlation by Region

datatable(
price_sensitivity %>%
mutate(cor_price_sales = round(cor_price_sales, 4)),
rownames = FALSE,
options = list(dom = "t", pageLength = 6)
)

Executive Summary for the CMO

BMW’s EV expansion strategy for 2026 must be guided by insights into:

• Regional EV momentum
  
• Model-level performance
  
• Electrification readiness
  
• Temporal sales intensity

Key Insights

1. EV Growth Is Uneven Across Regions
   • Asia and the Middle East show strong upward momentum → expansion
     
   • Europe and North America are plateauing → stabilize
     
   • Africa and South America show intermittent growth → targeted investment
2. Growth Is Driven by Region-Specific “Hero Models”
   • Optimize marketing toward top-performing EV models
     
   • Strengthen dealer support and inventory for these models
3. Fuel-Type Mix Reveals EV Readiness
   • High EV/Hybrid share → ready for EV-centric campaigns
     
   • Petrol/Diesel-heavy markets need education & infrastructure
4. Heatmap Highlights When and Where Momentum Occurs
   • Identify consistent leaders and emerging opportunity windows
     
   • Time campaigns when momentum is strongest

Call to Action

Use these interactive insights to guide:

• Regional budget allocation
  
• Model-specific marketing priorities
  
• Infrastructure collaboration planning
  
• Timing of EV campaigns for maximum ROI

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